Global Dynamics of Grassland FVC and LST and Spatial Distribution of Their Correlation (2001–2022)
Bibliographic record
Abstract
Fractional Vegetation Cover (FVC) and Land Surface Temperature (LST) are critical indicators for assessing grassland ecosystems. Based on global remote sensing data for FVC and LST from 2001 to 2022, this study employs the Mann-Kendall trend test and Spearman correlation analysis to explore the dynamic changes in and spatial distribution patterns of both variables. The results indicate that the FVC is increasing in regions such as Europe, the eastern southern Sahara, western India, eastern South America, western and southern North America, and central China. However, it is decreasing in southern Canada, the central United States, and northern Australia. Significant increases in LST are observed in subarctic regions and the Tibetan Plateau, attributed to polar warming effects associated with global climate change. Conversely, the LST is decreasing in central China, eastern coastal Australia, and southern Africa. The global FVC-LST relationship exhibits the following four distinct spatial distribution patterns: (1) FVC increase and LST increase (Type 1), (2) FVC increase and LST decrease (Type 2), (3) FVC decrease and LST increase (Type 3), and (4) FVC decrease and LST decrease (Type 4). Type 1, covering 33.72%, is primarily found in high-latitude and high-altitude areas, such as subarctic regions and the Tibetan Plateau. Type 2, the largest group (46.98%), is mainly located in eastern North America, eastern South America, and southern Africa. Type 3, which comprises 18.72%, is concentrated in arid and semi-arid regions, while Type 4, representing only 0.59%, lacks clear spatial distribution patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".